La traduction de l'article a été préparée spécialement pour les étudiants du cours "Industrial ML on Big Data"
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AutoVIML – Python, . , . AutoVIML, , , , – , , .. .
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AutoVIML
python-, autoviml
pip
.
pip install autoviml
AutoVIML
from autoviml.Auto_ViML import Auto_ViML
AutoVIML . , Kaggle. .
import pandas as pd
df = pd.read_csv('heart_d.csv')
df
, autoviml
, AutoVIML.
#Basic Example with all parameters
model, features, trainm, testm = Auto_ViML(
train,
target,
test,
sample_submission,
hyper_param="GS",
feature_reduction=True,
scoring_parameter="weighted-f1",
KMeans_Featurizer=False,
Boosting_Flag=False,
Binning_Flag=False,
Add_Poly=False,
Stacking_Flag=False,
Imbalanced_Flag=False,
verbose=0,
)
, AutoVIML, . , .
train
: , dataframe, dataframe. dataframe «df», «df».target
: . «TenYearCHD».test
: . ( ””), , AutoVIML .sample_submission
: , .hyper_param
: RandomizedSearchCV, , Grid Search CV. «RS».feature_reduction
: true, .scoring_parameter
: , . «Weighted-f1».KMeans_featurizer
: true false XGboost , .boosting_flag
: . false.binning_flag
: false, true, .add_poly
: false.stacking_flag
: false. true, , . false.Imbalanced_flag
: true, SMOTING.Verbose
: . 3.
AutoVIML.
model, features, trainm, testm = Auto_ViML(
train=df,
target="TenYearCHD",
test="",
sample_submission="",
hyper_param="RS",
feature_reduction=True,
scoring_parameter="weighted-f1",
KMeans_Featurizer=False,
Boosting_Flag=True,
Binning_Flag=False,
Add_Poly=False,
Stacking_Flag=True,
Imbalanced_Flag=True,
verbose=3
)
.
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, AutoVIML , , .
, AutoVIML, , . AutoVIML – , .